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L-diversity
''l''-diversity, also written as ''ℓ''-diversity, is a form of group based anonymization that is used to preserve privacy in data sets by reducing the granularity of a data representation. This reduction is a trade off that results in some loss of effectiveness of data management or mining algorithms in order to gain some privacy. The ''l''-diversity model is an extension of the ''k''-anonymity model which reduces the granularity of data representation using techniques including generalization and suppression such that any given record maps onto at least ''k-1'' other records in the data. The ''l''-diversity model handles some of the weaknesses in the ''k''-anonymity model where protected identities to the level of ''k''-individuals is not equivalent to protecting the corresponding sensitive values that were generalized or suppressed, especially when the sensitive values within a group exhibit homogeneity. The ''l''-diversity model adds the promotion of intra-group diversity for s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
T-closeness
''t''-closeness is a further refinement of ''l''-diversity group based anonymization that is used to preserve privacy in data sets by reducing the granularity of a data representation. This reduction is a trade off that results in some loss of effectiveness of data management or data mining algorithms in order to gain some privacy. The ''t''-closeness model extends the ''l''-diversity model by treating the values of an attribute distinctly by taking into account the distribution of data values for that attribute. Formal definition Given the existence of data breaches where sensitive attributes may be inferred based upon the distribution of values for ''l''-diverse data, the ''t''-closeness method was created to further ''l''-diversity by additionally maintaining the distribution of sensitive fields. The original paper by Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian defines ''t''-closeness as: Charu Aggarwal and Philip S. Yu further state in their book on privacy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Anonymization
Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous. Overview Data anonymization has been defined as a "process by which personal data is altered in such a way that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party." Data anonymization may enable the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization. In the context of medical data, anonymized data refers to data from which the patient cannot be identified by the recipient of the information. The name, address, and full postcode must be removed ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-anonymity
''k''-anonymity is a property possessed by certain anonymized data. The concept of ''k''-anonymity was first introduced by Latanya Sweeney and Pierangela Samarati in a paper published in 1998 as an attempt to solve the problem: "Given person-specific field-structured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful." A release of data is said to have the ''k''-anonymity property if the information for each person contained in the release cannot be distinguished from at least k - 1 individuals whose information also appear in the release. Unfortunately, the guarantees provided by k-anonymity are aspirational, not mathematical. Methods for ''k''-anonymization To use k-anonymity to process a dataset so that it can be released with privacy protection, a data scientist must first examine the dataset and decide if each attribute (column) is an ''identif ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Privacy
Privacy (, ) is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively. The domain of privacy partially overlaps with security, which can include the concepts of appropriate use and protection of information. Privacy may also take the form of bodily integrity. The right not to be subjected to unsanctioned invasions of privacy by the government, corporations, or individuals is part of many countries' privacy laws, and in some cases, constitutions. The concept of universal individual privacy is a modern concept primarily associated with Western culture, particularly British and North American, and remained virtually unknown in some cultures until recent times. Now, most cultures recognize the ability of individuals to withhold certain parts of personal information from wider society. With the rise of technology, the debate regarding privacy has shifted from a bodily sense to a digital sense. As th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data
In the pursuit of knowledge, data (; ) is a collection of discrete Value_(semiotics), values that convey information, describing quantity, qualitative property, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpretation (logic), interpreted. A datum is an individual value in a collection of data. Data is usually organized into structures such as table (information), tables that provide additional context and meaning, and which may themselves be used as data in larger structures. Data may be used as variable (research), variables in a computation, computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, economics, and in virtually every other form of human organizational activity. Examples of data sets include price indices (such as consumer price index), unemployment rates, literacy rates, and census data. In this context, data represents the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Management
Data management comprises all disciplines related to handling data as a valuable resource. Concept The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems." However, during this period, random access processing was not competitively fast, so those suggesting "process management" was more important than "data management" used batch processing time as their primary argument. As application software evolved into real-time, interactive usage, it became obvious that both management processes were important. If the data was not well defined, the data ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Differential Privacy
Differential privacy (DP) is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy. Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records whose information is in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible ev ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Anonymity
Anonymity describes situations where the acting person's identity is unknown. Some writers have argued that namelessness, though technically correct, does not capture what is more centrally at stake in contexts of anonymity. The important idea here is that a person be non-identifiable, unreachable, or untrackable. Anonymity is seen as a technique, or a way of realizing, a certain other values, such as privacy, or liberty. Over the past few years, anonymity tools used on the dark web by criminals and malicious users have drastically altered the ability of law enforcement to use conventional surveillance techniques. An important example for anonymity being not only protected, but enforced by law is the vote in free elections. In many other situations (like conversation between strangers, buying some product or service in a shop), anonymity is traditionally accepted as natural. There are also various situations in which a person might choose to withhold their identity. Acts of cha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
